Information Retrieval Based on Word Semantic Clustering

Chia-Yang Chang, Yan-Ting Lin, Shie-Jue Lee, Chih-Chin Lai
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Abstract

Information retrieval is an important topic in the modern age. With the advance of Internet, it is more and more easy to retrieve other people's writings or publications. However, how to retrieve desirable information efficiently is a challenging work. Traditional methods like vector space model or bag-of-words are short of providing a good solution due to the incapability of handling the semantics of words satisfactorily. In this paper, we propose a new method for information retrieval. We use Word2vec to transform the words into word vectors which are able to represent the semantic relationship among different words. By considering the semantic of words and clustering the word vectors into concepts, information retrieval can be done effectively.
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基于词语义聚类的信息检索
信息检索是当今时代的一个重要课题。随着互联网的发展,检索他人的著作或出版物变得越来越容易。然而,如何有效地检索需要的信息是一项具有挑战性的工作。传统的方法如向量空间模型、词袋模型等,由于不能很好地处理词的语义,不能很好地解决问题。本文提出了一种新的信息检索方法。我们使用Word2vec将单词转换成能够表示不同单词之间语义关系的单词向量。通过考虑词的语义,将词向量聚类成概念,可以有效地进行信息检索。
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